{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Planar data classification with one hidden layer\n",
    "\n",
    "Welcome to your week 3 programming assignment. It's time to build your first neural network, which will have a hidden layer. You will see a big difference between this model and the one you implemented using logistic regression. \n",
    "\n",
    "**You will learn how to:**\n",
    "- Implement a 2-class classification neural network with a single hidden layer\n",
    "- Use units with a non-linear activation function, such as tanh \n",
    "- Compute the cross entropy loss \n",
    "- Implement forward and backward propagation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Packages ##\n",
    "\n",
    "Let's first import all the packages that you will need during this assignment.\n",
    "- [numpy](www.numpy.org) is the fundamental package for scientific computing with Python.\n",
    "- [sklearn](http://scikit-learn.org/stable/) provides simple and efficient tools for data mining and data analysis. \n",
    "- [matplotlib](http://matplotlib.org) is a library for plotting graphs in Python.\n",
    "- testCases provides some test examples to assess the correctness of your functions\n",
    "- planar_utils provide various useful functions used in this assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Package imports\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from testCases import *\n",
    "import sklearn\n",
    "import sklearn.datasets\n",
    "import sklearn.linear_model\n",
    "from planar_utils import plot_decision_boundary, sigmoid, load_planar_dataset, load_extra_datasets\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "np.random.seed(1) # set a seed so that the results are consistent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Dataset ##\n",
    "\n",
    "First, let's get the dataset you will work on. The following code will load a \"flower\" 2-class dataset into variables `X` and `Y`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X, Y = load_planar_dataset() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the dataset using matplotlib. The data looks like a \"flower\" with some red (label y=0) and some blue (y=1) points. Your goal is to build a model to fit this data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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m1LbCUjY//DEDP3igHiyrfUqLrbz01FIKCyxYKxwYjTJzv4pn8vQuTL6qK2az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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df94734fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the data:\n",
    "plt.scatter(X[0, :], X[1, :], c=Y, s=40, cmap=plt.cm.Spectral);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have:\n",
    "    - a numpy-array (matrix) X that contains your features (x1, x2)\n",
    "    - a numpy-array (vector) Y that contains your labels (red:0, blue:1).\n",
    "\n",
    "Lets first get a better sense of what our data is like. \n",
    "\n",
    "**Exercise**: How many training examples do you have? In addition, what is the `shape` of the variables `X` and `Y`? \n",
    "\n",
    "**Hint**: How do you get the shape of a numpy array? [(help)](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.shape.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of X is: (2, 400)\n",
      "The shape of Y is: (1, 400)\n",
      "I have m = 400 training examples!\n"
     ]
    }
   ],
   "source": [
    "### START CODE HERE ### (≈ 3 lines of code)\n",
    "shape_X = X.shape\n",
    "shape_Y = Y.shape\n",
    "\n",
    "m = shape_X[1]  # training set size\n",
    "### END CODE HERE ###\n",
    "\n",
    "print ('The shape of X is: ' + str(shape_X))\n",
    "print ('The shape of Y is: ' + str(shape_Y))\n",
    "print ('I have m = %d training examples!' % (m))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "       \n",
    "<table style=\"width:20%\">\n",
    "  \n",
    "  <tr>\n",
    "    <td>**shape of X**</td>\n",
    "    <td> (2, 400) </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**shape of Y**</td>\n",
    "    <td>(1, 400) </td> \n",
    "  </tr>\n",
    "  \n",
    "    <tr>\n",
    "    <td>**m**</td>\n",
    "    <td> 400 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Simple Logistic Regression\n",
    "\n",
    "Before building a full neural network, lets first see how logistic regression performs on this problem. You can use sklearn's built-in functions to do that. Run the code below to train a logistic regression classifier on the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:547: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "# Train the logistic regression classifier\n",
    "clf = sklearn.linear_model.LogisticRegressionCV();\n",
    "clf.fit(X.T, Y.T);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can now plot the decision boundary of these models. Run the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression: 47 % (percentage of correctly labelled datapoints)\n"
     ]
    },
    {
     "data": {
      "image/png": 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BQZgPNNc/rsa7Z4nOrL+Z/Yyhr/UW2MU00svoBdj5EfoaisfmXuehhVOkfBFi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df936914a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the decision boundary for logistic regression\n",
    "plot_decision_boundary(lambda x: clf.predict(x), X, Y)\n",
    "plt.title(\"Logistic Regression\")\n",
    "\n",
    "# Print accuracy\n",
    "LR_predictions = clf.predict(X.T)\n",
    "print ('Accuracy of logistic regression: %d ' % float((np.dot(Y,LR_predictions) + np.dot(1-Y,1-LR_predictions))/float(Y.size)*100) +\n",
    "       '% ' + \"(percentage of correctly labelled datapoints)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:20%\">\n",
    "  <tr>\n",
    "    <td>**Accuracy**</td>\n",
    "    <td> 47% </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation**: The dataset is not linearly separable, so logistic regression doesn't perform well. Hopefully a neural network will do better. Let's try this now! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - Neural Network model\n",
    "\n",
    "Logistic regression did not work well on the \"flower dataset\". You are going to train a Neural Network with a single hidden layer.\n",
    "\n",
    "**Here is our model**:\n",
    "<img src=\"images/classification_kiank.png\" style=\"width:600px;height:300px;\">\n",
    "\n",
    "**Mathematically**:\n",
    "\n",
    "For one example $x^{(i)}$:\n",
    "$$z^{[1] (i)} =  W^{[1]} x^{(i)} + b^{[1] (i)}\\tag{1}$$ \n",
    "$$a^{[1] (i)} = \\tanh(z^{[1] (i)})\\tag{2}$$\n",
    "$$z^{[2] (i)} = W^{[2]} a^{[1] (i)} + b^{[2] (i)}\\tag{3}$$\n",
    "$$\\hat{y}^{(i)} = a^{[2] (i)} = \\sigma(z^{ [2] (i)})\\tag{4}$$\n",
    "$$y^{(i)}_{prediction} = \\begin{cases} 1 & \\mbox{if } a^{[2](i)} > 0.5 \\\\ 0 & \\mbox{otherwise } \\end{cases}\\tag{5}$$\n",
    "\n",
    "Given the predictions on all the examples, you can also compute the cost $J$ as follows: \n",
    "$$J = - \\frac{1}{m} \\sum\\limits_{i = 0}^{m} \\large\\left(\\small y^{(i)}\\log\\left(a^{[2] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[2] (i)}\\right)  \\large  \\right) \\small \\tag{6}$$\n",
    "\n",
    "**Reminder**: The general methodology to build a Neural Network is to:\n",
    "    1. Define the neural network structure ( # of input units,  # of hidden units, etc). \n",
    "    2. Initialize the model's parameters\n",
    "    3. Loop:\n",
    "        - Implement forward propagation\n",
    "        - Compute loss\n",
    "        - Implement backward propagation to get the gradients\n",
    "        - Update parameters (gradient descent)\n",
    "\n",
    "You often build helper functions to compute steps 1-3 and then merge them into one function we call `nn_model()`. Once you've built `nn_model()` and learnt the right parameters, you can make predictions on new data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 - Defining the neural network structure ####\n",
    "\n",
    "**Exercise**: Define three variables:\n",
    "    - n_x: the size of the input layer\n",
    "    - n_h: the size of the hidden layer (set this to 4) \n",
    "    - n_y: the size of the output layer\n",
    "\n",
    "**Hint**: Use shapes of X and Y to find n_x and n_y. Also, hard code the hidden layer size to be 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: layer_sizes\n",
    "\n",
    "def layer_sizes(X, Y):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    X -- input dataset of shape (input size, number of examples)\n",
    "    Y -- labels of shape (output size, number of examples)\n",
    "    \n",
    "    Returns:\n",
    "    n_x -- the size of the input layer\n",
    "    n_h -- the size of the hidden layer\n",
    "    n_y -- the size of the output layer\n",
    "    \"\"\"\n",
    "    ### START CODE HERE ### (≈ 3 lines of code)\n",
    "    n_x = X.shape[0] # size of input layer\n",
    "    n_h = 4\n",
    "    n_y = Y.shape[0] # size of output layer\n",
    "    ### END CODE HERE ###\n",
    "    return (n_x, n_h, n_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of the input layer is: n_x = 5\n",
      "The size of the hidden layer is: n_h = 4\n",
      "The size of the output layer is: n_y = 2\n"
     ]
    }
   ],
   "source": [
    "X_assess, Y_assess = layer_sizes_test_case()\n",
    "(n_x, n_h, n_y) = layer_sizes(X_assess, Y_assess)\n",
    "print(\"The size of the input layer is: n_x = \" + str(n_x))\n",
    "print(\"The size of the hidden layer is: n_h = \" + str(n_h))\n",
    "print(\"The size of the output layer is: n_y = \" + str(n_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output** (these are not the sizes you will use for your network, they are just used to assess the function you've just coded).\n",
    "\n",
    "<table style=\"width:20%\">\n",
    "  <tr>\n",
    "    <td>**n_x**</td>\n",
    "    <td> 5 </td> \n",
    "  </tr>\n",
    "  \n",
    "    <tr>\n",
    "    <td>**n_h**</td>\n",
    "    <td> 4 </td> \n",
    "  </tr>\n",
    "  \n",
    "    <tr>\n",
    "    <td>**n_y**</td>\n",
    "    <td> 2 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 - Initialize the model's parameters ####\n",
    "\n",
    "**Exercise**: Implement the function `initialize_parameters()`.\n",
    "\n",
    "**Instructions**:\n",
    "- Make sure your parameters' sizes are right. Refer to the neural network figure above if needed.\n",
    "- You will initialize the weights matrices with random values. \n",
    "    - Use: `np.random.randn(a,b) * 0.01` to randomly initialize a matrix of shape (a,b).\n",
    "- You will initialize the bias vectors as zeros. \n",
    "    - Use: `np.zeros((a,b))` to initialize a matrix of shape (a,b) with zeros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: initialize_parameters\n",
    "\n",
    "def initialize_parameters(n_x, n_h, n_y):\n",
    "    \"\"\"\n",
    "    Argument:\n",
    "    n_x -- size of the input layer\n",
    "    n_h -- size of the hidden layer\n",
    "    n_y -- size of the output layer\n",
    "    \n",
    "    Returns:\n",
    "    params -- python dictionary containing your parameters:\n",
    "                    W1 -- weight matrix of shape (n_h, n_x)\n",
    "                    b1 -- bias vector of shape (n_h, 1)\n",
    "                    W2 -- weight matrix of shape (n_y, n_h)\n",
    "                    b2 -- bias vector of shape (n_y, 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(2) # we set up a seed so that your output matches ours although the initialization is random.\n",
    "    \n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    W1 = np.random.randn(n_h,n_x) * 0.01\n",
    "    b1 = np.zeros((n_h,1))\n",
    "    W2 = np.random.randn(n_y,n_h) * 0.01\n",
    "    b2 = np.zeros((n_y,1))\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    assert (W1.shape == (n_h, n_x))\n",
    "    assert (b1.shape == (n_h, 1))\n",
    "    assert (W2.shape == (n_y, n_h))\n",
    "    assert (b2.shape == (n_y, 1))\n",
    "    \n",
    "    parameters = {\"W1\": W1,\n",
    "                  \"b1\": b1,\n",
    "                  \"W2\": W2,\n",
    "                  \"b2\": b2}\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[-0.00416758 -0.00056267]\n",
      " [-0.02136196  0.01640271]\n",
      " [-0.01793436 -0.00841747]\n",
      " [ 0.00502881 -0.01245288]]\n",
      "b1 = [[ 0.]\n",
      " [ 0.]\n",
      " [ 0.]\n",
      " [ 0.]]\n",
      "W2 = [[-0.01057952 -0.00909008  0.00551454  0.02292208]]\n",
      "b2 = [[ 0.]]\n"
     ]
    }
   ],
   "source": [
    "n_x, n_h, n_y = initialize_parameters_test_case()\n",
    "\n",
    "parameters = initialize_parameters(n_x, n_h, n_y)\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:90%\">\n",
    "  <tr>\n",
    "    <td>**W1**</td>\n",
    "    <td> [[-0.00416758 -0.00056267]\n",
    " [-0.02136196  0.01640271]\n",
    " [-0.01793436 -0.00841747]\n",
    " [ 0.00502881 -0.01245288]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**b1**</td>\n",
    "    <td> [[ 0.]\n",
    " [ 0.]\n",
    " [ 0.]\n",
    " [ 0.]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**W2**</td>\n",
    "    <td> [[-0.01057952 -0.00909008  0.00551454  0.02292208]]</td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**b2**</td>\n",
    "    <td> [[ 0.]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 - The Loop ####\n",
    "\n",
    "**Question**: Implement `forward_propagation()`.\n",
    "\n",
    "**Instructions**:\n",
    "- Look above at the mathematical representation of your classifier.\n",
    "- You can use the function `sigmoid()`. It is built-in (imported) in the notebook.\n",
    "- You can use the function `np.tanh()`. It is part of the numpy library.\n",
    "- The steps you have to implement are:\n",
    "    1. Retrieve each parameter from the dictionary \"parameters\" (which is the output of `initialize_parameters()`) by using `parameters[\"..\"]`.\n",
    "    2. Implement Forward Propagation. Compute $Z^{[1]}, A^{[1]}, Z^{[2]}$ and $A^{[2]}$ (the vector of all your predictions on all the examples in the training set).\n",
    "- Values needed in the backpropagation are stored in \"`cache`\". The `cache` will be given as an input to the backpropagation function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: forward_propagation\n",
    "\n",
    "def forward_propagation(X, parameters):\n",
    "    \"\"\"\n",
    "    Argument:\n",
    "    X -- input data of size (n_x, m)\n",
    "    parameters -- python dictionary containing your parameters (output of initialization function)\n",
    "    \n",
    "    Returns:\n",
    "    A2 -- The sigmoid output of the second activation\n",
    "    cache -- a dictionary containing \"Z1\", \"A1\", \"Z2\" and \"A2\"\n",
    "    \"\"\"\n",
    "    # Retrieve each parameter from the dictionary \"parameters\"\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    b2 = parameters[\"b2\"]\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Implement Forward Propagation to calculate A2 (probabilities)\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    Z1 = np.dot(W1,X) + b1\n",
    "    A1 = np.tanh(Z1)\n",
    "    Z2 = np.dot(W2,A1) + b2\n",
    "    A2 = sigmoid(Z2)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    assert(A2.shape == (1, X.shape[1]))\n",
    "    \n",
    "    cache = {\"Z1\": Z1,\n",
    "             \"A1\": A1,\n",
    "             \"Z2\": Z2,\n",
    "             \"A2\": A2}\n",
    "    \n",
    "    return A2, cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.000499755777742 -0.000496963353232 0.000438187450959 0.500109546852\n"
     ]
    }
   ],
   "source": [
    "X_assess, parameters = forward_propagation_test_case()\n",
    "\n",
    "A2, cache = forward_propagation(X_assess, parameters)\n",
    "\n",
    "# Note: we use the mean here just to make sure that your output matches ours. \n",
    "print(np.mean(cache['Z1']) ,np.mean(cache['A1']),np.mean(cache['Z2']),np.mean(cache['A2']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "<table style=\"width:55%\">\n",
    "  <tr>\n",
    "    <td> -0.000499755777742 -0.000496963353232 0.000438187450959 0.500109546852 </td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you have computed $A^{[2]}$ (in the Python variable \"`A2`\"), which contains $a^{[2](i)}$ for every example, you can compute the cost function as follows:\n",
    "\n",
    "$$J = - \\frac{1}{m} \\sum\\limits_{i = 0}^{m} \\large{(} \\small y^{(i)}\\log\\left(a^{[2] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[2] (i)}\\right) \\large{)} \\small\\tag{13}$$\n",
    "\n",
    "**Exercise**: Implement `compute_cost()` to compute the value of the cost $J$.\n",
    "\n",
    "**Instructions**:\n",
    "- There are many ways to implement the cross-entropy loss. To help you, we give you how we would have implemented\n",
    "$- \\sum\\limits_{i=0}^{m}  y^{(i)}\\log(a^{[2](i)})$:\n",
    "```python\n",
    "logprobs = np.multiply(np.log(A2),Y)\n",
    "cost = - np.sum(logprobs)                # no need to use a for loop!\n",
    "```\n",
    "\n",
    "(you can use either `np.multiply()` and then `np.sum()` or directly `np.dot()`).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: compute_cost\n",
    "\n",
    "def compute_cost(A2, Y, parameters):\n",
    "    \"\"\"\n",
    "    Computes the cross-entropy cost given in equation (13)\n",
    "    \n",
    "    Arguments:\n",
    "    A2 -- The sigmoid output of the second activation, of shape (1, number of examples)\n",
    "    Y -- \"true\" labels vector of shape (1, number of examples)\n",
    "    parameters -- python dictionary containing your parameters W1, b1, W2 and b2\n",
    "    \n",
    "    Returns:\n",
    "    cost -- cross-entropy cost given equation (13)\n",
    "    \"\"\"\n",
    "    \n",
    "    m = Y.shape[1] # number of example\n",
    "\n",
    "    # Compute the cross-entropy cost\n",
    "     ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    logprobs = Y*np.log(A2) + (1-Y)* np.log(1-A2)\n",
    "    cost = -1/m * np.sum(logprobs)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    cost = np.squeeze(cost)     # makes sure cost is the dimension we expect. \n",
    "                                # E.g., turns [[17]] into 17 \n",
    "    assert(isinstance(cost, float))\n",
    "    \n",
    "    return cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cost = 0.692919893776\n"
     ]
    }
   ],
   "source": [
    "A2, Y_assess, parameters = compute_cost_test_case()\n",
    "\n",
    "print(\"cost = \" + str(compute_cost(A2, Y_assess, parameters)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "<table style=\"width:20%\">\n",
    "  <tr>\n",
    "    <td>**cost**</td>\n",
    "    <td> 0.692919893776 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the cache computed during forward propagation, you can now implement backward propagation.\n",
    "\n",
    "**Question**: Implement the function `backward_propagation()`.\n",
    "\n",
    "**Instructions**:\n",
    "Backpropagation is usually the hardest (most mathematical) part in deep learning. To help you, here again is the slide from the lecture on backpropagation. You'll want to use the six equations on the right of this slide, since you are building a vectorized implementation.  \n",
    "\n",
    "<img src=\"images/grad_summary.png\" style=\"width:600px;height:300px;\">\n",
    "\n",
    "<!--\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)} } = \\frac{1}{m} (a^{[2](i)} - y^{(i)})$\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial W_2 } = \\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)} } a^{[1] (i) T} $\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial b_2 } = \\sum_i{\\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)}}}$\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial z_{1}^{(i)} } =  W_2^T \\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)} } * ( 1 - a^{[1] (i) 2}) $\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial W_1 } = \\frac{\\partial \\mathcal{J} }{ \\partial z_{1}^{(i)} }  X^T $\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} _i }{ \\partial b_1 } = \\sum_i{\\frac{\\partial \\mathcal{J} }{ \\partial z_{1}^{(i)}}}$\n",
    "\n",
    "- Note that $*$ denotes elementwise multiplication.\n",
    "- The notation you will use is common in deep learning coding:\n",
    "    - dW1 = $\\frac{\\partial \\mathcal{J} }{ \\partial W_1 }$\n",
    "    - db1 = $\\frac{\\partial \\mathcal{J} }{ \\partial b_1 }$\n",
    "    - dW2 = $\\frac{\\partial \\mathcal{J} }{ \\partial W_2 }$\n",
    "    - db2 = $\\frac{\\partial \\mathcal{J} }{ \\partial b_2 }$\n",
    "    \n",
    "!-->\n",
    "\n",
    "- Tips:\n",
    "    - To compute dZ1 you'll need to compute $g^{[1]'}(Z^{[1]})$. Since $g^{[1]}(.)$ is the tanh activation function, if $a = g^{[1]}(z)$ then $g^{[1]'}(z) = 1-a^2$. So you can compute \n",
    "    $g^{[1]'}(Z^{[1]})$ using `(1 - np.power(A1, 2))`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: backward_propagation\n",
    "\n",
    "def backward_propagation(parameters, cache, X, Y):\n",
    "    \"\"\"\n",
    "    Implement the backward propagation using the instructions above.\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing our parameters \n",
    "    cache -- a dictionary containing \"Z1\", \"A1\", \"Z2\" and \"A2\".\n",
    "    X -- input data of shape (2, number of examples)\n",
    "    Y -- \"true\" labels vector of shape (1, number of examples)\n",
    "    \n",
    "    Returns:\n",
    "    grads -- python dictionary containing your gradients with respect to different parameters\n",
    "    \"\"\"\n",
    "    m = X.shape[1]\n",
    "    \n",
    "    # First, retrieve W1 and W2 from the dictionary \"parameters\".\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    W1 = parameters[\"W1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    ### END CODE HERE ###\n",
    "        \n",
    "    # Retrieve also A1 and A2 from dictionary \"cache\".\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    A1 = cache[\"A1\"]\n",
    "    A2 = cache[\"A2\"]\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Backward propagation: calculate dW1, db1, dW2, db2. \n",
    "    ### START CODE HERE ### (≈ 6 lines of code, corresponding to 6 equations on slide above)\n",
    "    dZ2= A2 - Y\n",
    "    dW2 = 1 / m * np.dot(dZ2,A1.T)\n",
    "    db2 = 1 / m * np.sum(dZ2,axis=1,keepdims=True)\n",
    "    dZ1 = np.dot(W2.T,dZ2) * (1-np.power(A1,2))\n",
    "    dW1 = 1 / m * np.dot(dZ1,X.T)\n",
    "    db1 = 1 / m * np.sum(dZ1,axis=1,keepdims=True)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    grads = {\"dW1\": dW1,\n",
    "             \"db1\": db1,\n",
    "             \"dW2\": dW2,\n",
    "             \"db2\": db2}\n",
    "    \n",
    "    return grads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dW1 = [[ 0.01018708 -0.00708701]\n",
      " [ 0.00873447 -0.0060768 ]\n",
      " [-0.00530847  0.00369379]\n",
      " [-0.02206365  0.01535126]]\n",
      "db1 = [[-0.00069728]\n",
      " [-0.00060606]\n",
      " [ 0.000364  ]\n",
      " [ 0.00151207]]\n",
      "dW2 = [[ 0.00363613  0.03153604  0.01162914 -0.01318316]]\n",
      "db2 = [[ 0.06589489]]\n"
     ]
    }
   ],
   "source": [
    "parameters, cache, X_assess, Y_assess = backward_propagation_test_case()\n",
    "\n",
    "grads = backward_propagation(parameters, cache, X_assess, Y_assess)\n",
    "print (\"dW1 = \"+ str(grads[\"dW1\"]))\n",
    "print (\"db1 = \"+ str(grads[\"db1\"]))\n",
    "print (\"dW2 = \"+ str(grads[\"dW2\"]))\n",
    "print (\"db2 = \"+ str(grads[\"db2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected output**:\n",
    "\n",
    "\n",
    "\n",
    "<table style=\"width:80%\">\n",
    "  <tr>\n",
    "    <td>**dW1**</td>\n",
    "    <td> [[ 0.01018708 -0.00708701]\n",
    " [ 0.00873447 -0.0060768 ]\n",
    " [-0.00530847  0.00369379]\n",
    " [-0.02206365  0.01535126]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**db1**</td>\n",
    "    <td>  [[-0.00069728]\n",
    " [-0.00060606]\n",
    " [ 0.000364  ]\n",
    " [ 0.00151207]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**dW2**</td>\n",
    "    <td> [[ 0.00363613  0.03153604  0.01162914 -0.01318316]] </td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**db2**</td>\n",
    "    <td> [[ 0.06589489]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question**: Implement the update rule. Use gradient descent. You have to use (dW1, db1, dW2, db2) in order to update (W1, b1, W2, b2).\n",
    "\n",
    "**General gradient descent rule**: $ \\theta = \\theta - \\alpha \\frac{\\partial J }{ \\partial \\theta }$ where $\\alpha$ is the learning rate and $\\theta$ represents a parameter.\n",
    "\n",
    "**Illustration**: The gradient descent algorithm with a good learning rate (converging) and a bad learning rate (diverging). Images courtesy of Adam Harley.\n",
    "\n",
    "<img src=\"images/sgd.gif\" style=\"width:400;height:400;\"> <img src=\"images/sgd_bad.gif\" style=\"width:400;height:400;\">\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: update_parameters\n",
    "\n",
    "def update_parameters(parameters, grads, learning_rate = 1.2):\n",
    "    \"\"\"\n",
    "    Updates parameters using the gradient descent update rule given above\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing your parameters \n",
    "    grads -- python dictionary containing your gradients \n",
    "    \n",
    "    Returns:\n",
    "    parameters -- python dictionary containing your updated parameters \n",
    "    \"\"\"\n",
    "    # Retrieve each parameter from the dictionary \"parameters\"\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    b2 = parameters[\"b2\"]\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Retrieve each gradient from the dictionary \"grads\"\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    dW1 = grads[\"dW1\"]\n",
    "    db1 = grads[\"db1\"]\n",
    "    dW2 = grads[\"dW2\"]\n",
    "    db2 = grads[\"db2\"]\n",
    "    ## END CODE HERE ###\n",
    "    \n",
    "    # Update rule for each parameter\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    W1 = W1 - learning_rate * dW1\n",
    "    b1 = b1 - learning_rate * db1\n",
    "    W2 = W2 - learning_rate * dW2\n",
    "    b2 = b2 - learning_rate * db2\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    parameters = {\"W1\": W1,\n",
    "                  \"b1\": b1,\n",
    "                  \"W2\": W2,\n",
    "                  \"b2\": b2}\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[-0.00643025  0.01936718]\n",
      " [-0.02410458  0.03978052]\n",
      " [-0.01653973 -0.02096177]\n",
      " [ 0.01046864 -0.05990141]]\n",
      "b1 = [[ -1.02420756e-06]\n",
      " [  1.27373948e-05]\n",
      " [  8.32996807e-07]\n",
      " [ -3.20136836e-06]]\n",
      "W2 = [[-0.01041081 -0.04463285  0.01758031  0.04747113]]\n",
      "b2 = [[ 0.00010457]]\n"
     ]
    }
   ],
   "source": [
    "parameters, grads = update_parameters_test_case()\n",
    "parameters = update_parameters(parameters, grads)\n",
    "\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "\n",
    "<table style=\"width:80%\">\n",
    "  <tr>\n",
    "    <td>**W1**</td>\n",
    "    <td> [[-0.00643025  0.01936718]\n",
    " [-0.02410458  0.03978052]\n",
    " [-0.01653973 -0.02096177]\n",
    " [ 0.01046864 -0.05990141]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**b1**</td>\n",
    "    <td> [[ -1.02420756e-06]\n",
    " [  1.27373948e-05]\n",
    " [  8.32996807e-07]\n",
    " [ -3.20136836e-06]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**W2**</td>\n",
    "    <td> [[-0.01041081 -0.04463285  0.01758031  0.04747113]] </td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**b2**</td>\n",
    "    <td> [[ 0.00010457]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 - Integrate parts 4.1, 4.2 and 4.3 in nn_model() ####\n",
    "\n",
    "**Question**: Build your neural network model in `nn_model()`.\n",
    "\n",
    "**Instructions**: The neural network model has to use the previous functions in the right order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: nn_model\n",
    "\n",
    "def nn_model(X, Y, n_h, num_iterations = 10000, print_cost=False):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    X -- dataset of shape (2, number of examples)\n",
    "    Y -- labels of shape (1, number of examples)\n",
    "    n_h -- size of the hidden layer\n",
    "    num_iterations -- Number of iterations in gradient descent loop\n",
    "    print_cost -- if True, print the cost every 1000 iterations\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- parameters learnt by the model. They can then be used to predict.\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(3)\n",
    "    n_x = layer_sizes(X, Y)[0]\n",
    "    n_y = layer_sizes(X, Y)[2]\n",
    "    \n",
    "    # Initialize parameters, then retrieve W1, b1, W2, b2. Inputs: \"n_x, n_h, n_y\". Outputs = \"W1, b1, W2, b2, parameters\".\n",
    "    ### START CODE HERE ### (≈ 5 lines of code)\n",
    "    parameters = initialize_parameters(n_x, n_h, n_y)\n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    b2 = parameters[\"b2\"]\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Loop (gradient descent)\n",
    "\n",
    "    for i in range(0, num_iterations):\n",
    "         \n",
    "        ### START CODE HERE ### (≈ 4 lines of code)\n",
    "        # Forward propagation. Inputs: \"X, parameters\". Outputs: \"A2, cache\".\n",
    "        A2, cache = forward_propagation(X, parameters)\n",
    "        \n",
    "        # Cost function. Inputs: \"A2, Y, parameters\". Outputs: \"cost\".\n",
    "        cost = compute_cost(A2, Y, parameters)\n",
    " \n",
    "        # Backpropagation. Inputs: \"parameters, cache, X, Y\". Outputs: \"grads\".\n",
    "        grads = backward_propagation(parameters, cache, X, Y)\n",
    " \n",
    "        # Gradient descent parameter update. Inputs: \"parameters, grads\". Outputs: \"parameters\".\n",
    "        parameters = update_parameters(parameters, grads)\n",
    "        \n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Print the cost every 1000 iterations\n",
    "        if print_cost and i % 1000 == 0:\n",
    "            print (\"Cost after iteration %i: %f\" %(i, cost))\n",
    "\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:20: RuntimeWarning: divide by zero encountered in log\n",
      "D:\\code\\deeplearning.ai作业\\02-课后作业\\01-第一课 神经网络和深度学习\\第一课第三周编程作业\\第一课第三周编程作业\\assignment3\\planar_utils.py:34: RuntimeWarning: overflow encountered in exp\n",
      "  s = 1/(1+np.exp(-x))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[-4.18494482  5.33220319]\n",
      " [-7.52989354  1.24306197]\n",
      " [-4.19295428  5.32631786]\n",
      " [ 7.52983748 -1.24309404]]\n",
      "b1 = [[ 2.32926815]\n",
      " [ 3.7945905 ]\n",
      " [ 2.33002544]\n",
      " [-3.79468791]]\n",
      "W2 = [[-6033.83672179 -6008.12981272 -6033.10095329  6008.06636901]]\n",
      "b2 = [[-52.66607704]]\n"
     ]
    }
   ],
   "source": [
    "X_assess, Y_assess = nn_model_test_case()\n",
    "\n",
    "parameters = nn_model(X_assess, Y_assess, 4, num_iterations=10000, print_cost=False)\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:90%\">\n",
    "  <tr>\n",
    "    <td>**W1**</td>\n",
    "    <td> [[-4.18494056  5.33220609]\n",
    " [-7.52989382  1.24306181]\n",
    " [-4.1929459   5.32632331]\n",
    " [ 7.52983719 -1.24309422]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**b1**</td>\n",
    "    <td> [[ 2.32926819]\n",
    " [ 3.79458998]\n",
    " [ 2.33002577]\n",
    " [-3.79468846]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**W2**</td>\n",
    "    <td> [[-6033.83672146 -6008.12980822 -6033.10095287  6008.06637269]] </td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**b2**</td>\n",
    "    <td> [[-52.66607724]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5 Predictions\n",
    "\n",
    "**Question**: Use your model to predict by building predict().\n",
    "Use forward propagation to predict results.\n",
    "\n",
    "**Reminder**: predictions = $y_{prediction} = \\mathbb 1 \\text{{activation > 0.5}} = \\begin{cases}\n",
    "      1 & \\text{if}\\ activation > 0.5 \\\\\n",
    "      0 & \\text{otherwise}\n",
    "    \\end{cases}$  \n",
    "    \n",
    "As an example, if you would like to set the entries of a matrix X to 0 and 1 based on a threshold you would do: ```X_new = (X > threshold)```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: predict\n",
    "\n",
    "def predict(parameters, X):\n",
    "    \"\"\"\n",
    "    Using the learned parameters, predicts a class for each example in X\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing your parameters \n",
    "    X -- input data of size (n_x, m)\n",
    "    \n",
    "    Returns\n",
    "    predictions -- vector of predictions of our model (red: 0 / blue: 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    # Computes probabilities using forward propagation, and classifies to 0/1 using 0.5 as the threshold.\n",
    "  ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    A2, cache = forward_propagation(X, parameters)\n",
    "    predictions = np.round(A2)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions mean = 0.666666666667\n"
     ]
    }
   ],
   "source": [
    "parameters, X_assess = predict_test_case()\n",
    "\n",
    "predictions = predict(parameters, X_assess)\n",
    "print(\"predictions mean = \" + str(np.mean(predictions)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "\n",
    "<table style=\"width:40%\">\n",
    "  <tr>\n",
    "    <td>**predictions mean**</td>\n",
    "    <td> 0.666666666667 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is time to run the model and see how it performs on a planar dataset. Run the following code to test your model with a single hidden layer of $n_h$ hidden units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.693048\n",
      "Cost after iteration 1000: 0.288083\n",
      "Cost after iteration 2000: 0.254385\n",
      "Cost after iteration 3000: 0.233864\n",
      "Cost after iteration 4000: 0.226792\n",
      "Cost after iteration 5000: 0.222644\n",
      "Cost after iteration 6000: 0.219731\n",
      "Cost after iteration 7000: 0.217504\n",
      "Cost after iteration 8000: 0.219504\n",
      "Cost after iteration 9000: 0.218571\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1df985535c0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nEAgBDXZaMIgIhgG1auftttObOjkiwp3Fc8z2HWjf5rpMVOYgvvHzKaWYvFil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\nOMztt9xPI7y/BDOj4SJFtMP05RkmxXSGjek5MotLAxeEUNVBBBjQNQn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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df9908fb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build a model with a n_h-dimensional hidden layer\n",
    "parameters = nn_model(X, Y, n_h = 4, num_iterations = 10000, print_cost=True)\n",
    "\n",
    "# Plot the decision boundary\n",
    "plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)\n",
    "plt.title(\"Decision Boundary for hidden layer size \" + str(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:40%\">\n",
    "  <tr>\n",
    "    <td>**Cost after iteration 9000**</td>\n",
    "    <td> 0.218607 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 90%\n"
     ]
    }
   ],
   "source": [
    "# Print accuracy\n",
    "predictions = predict(parameters, X)\n",
    "print ('Accuracy: %d' % float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100) + '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table style=\"width:15%\">\n",
    "  <tr>\n",
    "    <td>**Accuracy**</td>\n",
    "    <td> 90% </td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy is really high compared to Logistic Regression. The model has learnt the leaf patterns of the flower! Neural networks are able to learn even highly non-linear decision boundaries, unlike logistic regression. \n",
    "\n",
    "Now, let's try out several hidden layer sizes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.6 - Tuning hidden layer size (optional/ungraded exercise) ###\n",
    "\n",
    "Run the following code. It may take 1-2 minutes. You will observe different behaviors of the model for various hidden layer sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for 1 hidden units: 67.5 %\n",
      "Accuracy for 2 hidden units: 67.25 %\n",
      "Accuracy for 3 hidden units: 90.75 %\n",
      "Accuracy for 4 hidden units: 90.5 %\n",
      "Accuracy for 5 hidden units: 91.25 %\n",
      "Accuracy for 10 hidden units: 90.25 %\n",
      "Accuracy for 20 hidden units: 90.0 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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lBa+5/MUw4NzFGPnNgPXVzp3YMJxP58U4CMIEOqlR01pV73s2oWXBxAUH39Mo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CL5bg6GVLRVcvPCUdhZyqei3PnHN+m8+Q7sAmV4cijh5vBYkpHWXDff/vobeNLpPDff/tobvPHd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d3MJHqu+P7XUvws7mgJs5UCn5kevjLjsCTDfRy92QTB1RLCLRwYxRxOIUrfZg2kup6AME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hhGH3cUgQe5cZQ7IRkNtok2wGnc9BkLAJXaunYsDAwMZY2rEIko4EsUIIIYQ4diTyOYmUojqS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\nLhsUv2Nxe0atNJAoNgOIqAMlqKf8+bGxqo30Zg3Rqt3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jKX/EkW6d2+t1mN8L+M/V2GoV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\nERFRqPxPpYmwhVgqGAIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df985a25c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This may take about 2 minutes to run\n",
    "\n",
    "plt.figure(figsize=(16, 32))\n",
    "hidden_layer_sizes = [1, 2, 3, 4, 5, 10, 20]\n",
    "for i, n_h in enumerate(hidden_layer_sizes):\n",
    "    plt.subplot(5, 2, i+1)\n",
    "    plt.title('Hidden Layer of size %d' % n_h)\n",
    "    parameters = nn_model(X, Y, n_h, num_iterations = 5000)\n",
    "    plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)\n",
    "    predictions = predict(parameters, X)\n",
    "    accuracy = float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100)\n",
    "    print (\"Accuracy for {} hidden units: {} %\".format(n_h, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation**:\n",
    "- The larger models (with more hidden units) are able to fit the training set better, until eventually the largest models overfit the data. \n",
    "- The best hidden layer size seems to be around n_h = 5. Indeed, a value around here seems to  fits the data well without also incurring noticable overfitting.\n",
    "- You will also learn later about regularization, which lets you use very large models (such as n_h = 50) without much overfitting. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Optional questions**:\n",
    "\n",
    "**Note**: Remember to submit the assignment but clicking the blue \"Submit Assignment\" button at the upper-right. \n",
    "\n",
    "Some optional/ungraded questions that you can explore if you wish: \n",
    "- What happens when you change the tanh activation for a sigmoid activation or a ReLU activation?\n",
    "- Play with the learning_rate. What happens?\n",
    "- What if we change the dataset? (See part 5 below!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**You've learnt to:**\n",
    "- Build a complete neural network with a hidden layer\n",
    "- Make a good use of a non-linear unit\n",
    "- Implemented forward propagation and backpropagation, and trained a neural network\n",
    "- See the impact of varying the hidden layer size, including overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice work! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5) Performance on other datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "If you want, you can rerun the whole notebook (minus the dataset part) for each of the following datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Vq9WEzW4mPMLKQ0+OQTWp3HHfcKw2E+rxNzpVFSiKQFUrv5WpqkK3nrGNUh2k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\n+nRaHt9/vY/jRz25XabN7klChxbVdnwpJe/9exNrVx5FaJ7FalJ6nkL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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df9908fe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Datasets\n",
    "noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure = load_extra_datasets()\n",
    "\n",
    "datasets = {\"noisy_circles\": noisy_circles,\n",
    "            \"noisy_moons\": noisy_moons,\n",
    "            \"blobs\": blobs,\n",
    "            \"gaussian_quantiles\": gaussian_quantiles}\n",
    "\n",
    "### START CODE HERE ### (choose your dataset)\n",
    "dataset = \"gaussian_quantiles\"\n",
    "### END CODE HERE ###\n",
    "\n",
    "X, Y = datasets[dataset]\n",
    "X, Y = X.T, Y.reshape(1, Y.shape[0])\n",
    "\n",
    "# make blobs binary\n",
    "if dataset == \"blobs\":\n",
    "    Y = Y%2\n",
    "\n",
    "# Visualize the data\n",
    "plt.scatter(X[0, :], X[1, :], c=Y, s=40, cmap=plt.cm.Spectral);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congrats on finishing this Programming Assignment!\n",
    "\n",
    "Reference:\n",
    "- http://scs.ryerson.ca/~aharley/neural-networks/\n",
    "- http://cs231n.github.io/neural-networks-case-study/"
   ]
  }
 ],
 "metadata": {
  "coursera": {
   "course_slug": "neural-networks-deep-learning",
   "graded_item_id": "wRuwL",
   "launcher_item_id": "NI888"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
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